// Performance bottleneck identification and analysis
use crate::performance::{PerformanceReport, SystemMetric, MemoryMetric};
use crate::Result;
use serde::{Deserialize, Serialize};
use std::time::{Duration, SystemTime};

/// Performance bottleneck type
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum BottleneckType {
    /// CPU bottleneck
    CPU,
    /// Memory bottleneck
    Memory,
    /// I/O bottleneck
    IO,
    /// Network bottleneck
    Network,
    /// Disk bottleneck
    Disk,
    /// Application-specific bottleneck
    Application,
}

/// Severity level of performance issue
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
pub enum Severity {
    Low,
    Medium,
    High,
    Critical,
}

/// Performance bottleneck identification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceBottleneck {
    /// Type of bottleneck
    pub bottleneck_type: BottleneckType,
    /// Severity level
    pub severity: Severity,
    /// Description of the issue
    pub description: String,
    /// Affected metric name
    pub metric_name: String,
    /// Current value
    pub current_value: f64,
    /// Expected/optimal value
    pub expected_value: f64,
    /// Impact percentage
    pub impact_percentage: f64,
    /// Recommendations for fixing
    pub recommendations: Vec<String>,
    /// Detection timestamp
    pub detected_at: SystemTime,
}

/// Performance analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAnalysis {
    /// Identified bottlenecks
    pub bottlenecks: Vec<PerformanceBottleneck>,
    /// Overall performance score (0-100)
    pub performance_score: f64,
    /// Performance trends
    pub trends: PerformanceTrends,
    /// Optimization suggestions
    pub optimization_suggestions: Vec<OptimizationSuggestion>,
    /// Analysis timestamp
    pub analyzed_at: SystemTime,
}

/// Performance trends analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTrends {
    /// CPU usage trend
    pub cpu_trend: TrendDirection,
    /// Memory usage trend
    pub memory_trend: TrendDirection,
    /// Response time trend
    pub response_time_trend: TrendDirection,
    /// Error rate trend
    pub error_rate_trend: TrendDirection,
}

/// Trend direction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TrendDirection {
    Improving,
    Stable,
    Degrading,
    Volatile,
}

/// Optimization suggestion
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSuggestion {
    /// Category of optimization
    pub category: String,
    /// Priority level
    pub priority: Severity,
    /// Description
    pub description: String,
    /// Expected impact
    pub expected_impact: String,
    /// Implementation difficulty
    pub difficulty: String,
    /// Estimated time to implement
    pub estimated_time: String,
}

/// Bottleneck analyzer configuration
#[derive(Debug, Clone)]
pub struct AnalyzerConfig {
    /// CPU usage thresholds
    pub cpu_thresholds: ThresholdConfig,
    /// Memory usage thresholds
    pub memory_thresholds: ThresholdConfig,
    /// Response time thresholds (ms)
    pub response_time_thresholds: ThresholdConfig,
    /// Minimum samples for trend analysis
    pub min_samples_for_trend: usize,
    /// Analysis window duration
    pub analysis_window: Duration,
}

/// Threshold configuration
#[derive(Debug, Clone)]
pub struct ThresholdConfig {
    pub low: f64,
    pub medium: f64,
    pub high: f64,
    pub critical: f64,
}

impl Default for AnalyzerConfig {
    fn default() -> Self {
        Self {
            cpu_thresholds: ThresholdConfig {
                low: 50.0,
                medium: 70.0,
                high: 85.0,
                critical: 95.0,
            },
            memory_thresholds: ThresholdConfig {
                low: 60.0,
                medium: 75.0,
                high: 90.0,
                critical: 98.0,
            },
            response_time_thresholds: ThresholdConfig {
                low: 100.0,
                medium: 500.0,
                high: 1000.0,
                critical: 5000.0,
            },
            min_samples_for_trend: 10,
            analysis_window: Duration::from_secs(300), // 5 minutes
        }
    }
}

/// Performance bottleneck analyzer
pub struct BottleneckAnalyzer {
    config: AnalyzerConfig,
    historical_data: Vec<PerformanceReport>,
}

impl BottleneckAnalyzer {
    /// Create a new bottleneck analyzer
    pub fn new() -> Self {
        Self {
            config: AnalyzerConfig::default(),
            historical_data: Vec::new(),
        }
    }

    /// Create analyzer with custom configuration
    pub fn with_config(config: AnalyzerConfig) -> Self {
        Self {
            config,
            historical_data: Vec::new(),
        }
    }

    /// Add performance data for analysis
    pub fn add_performance_data(&mut self, report: PerformanceReport) {
        self.historical_data.push(report);
        
        // Keep only data within analysis window
        let cutoff = SystemTime::now() - self.config.analysis_window;
        self.historical_data.retain(|report| report.timestamp >= cutoff);
    }

    /// Analyze current performance and identify bottlenecks
    pub fn analyze_performance(&self) -> Result<PerformanceAnalysis> {
        if self.historical_data.is_empty() {
            return Ok(PerformanceAnalysis {
                bottlenecks: Vec::new(),
                performance_score: 100.0,
                trends: PerformanceTrends {
                    cpu_trend: TrendDirection::Stable,
                    memory_trend: TrendDirection::Stable,
                    response_time_trend: TrendDirection::Stable,
                    error_rate_trend: TrendDirection::Stable,
                },
                optimization_suggestions: Vec::new(),
                analyzed_at: SystemTime::now(),
            });
        }

        let latest_report = &self.historical_data[self.historical_data.len() - 1];
        
        // Identify bottlenecks
        let bottlenecks = self.identify_bottlenecks(latest_report)?;
        
        // Calculate performance score
        let performance_score = self.calculate_performance_score(latest_report, &bottlenecks);
        
        // Analyze trends
        let trends = self.analyze_trends()?;
        
        // Generate optimization suggestions
        let optimization_suggestions = self.generate_optimization_suggestions(&bottlenecks, &trends);

        Ok(PerformanceAnalysis {
            bottlenecks,
            performance_score,
            trends,
            optimization_suggestions,
            analyzed_at: SystemTime::now(),
        })
    }

    /// Identify performance bottlenecks
    fn identify_bottlenecks(&self, report: &PerformanceReport) -> Result<Vec<PerformanceBottleneck>> {
        let mut bottlenecks = Vec::new();

        // Analyze system metrics
        if let Some(latest_system) = self.get_latest_system_metric(report) {
            // CPU bottleneck analysis
            if let Some(cpu_bottleneck) = self.analyze_cpu_bottleneck(&latest_system) {
                bottlenecks.push(cpu_bottleneck);
            }

            // Memory bottleneck analysis
            if let Some(memory_bottleneck) = self.analyze_memory_bottleneck(&latest_system) {
                bottlenecks.push(memory_bottleneck);
            }

            // Disk bottleneck analysis
            if let Some(disk_bottleneck) = self.analyze_disk_bottleneck(&latest_system) {
                bottlenecks.push(disk_bottleneck);
            }

            // Network bottleneck analysis
            if let Some(network_bottleneck) = self.analyze_network_bottleneck(&latest_system) {
                bottlenecks.push(network_bottleneck);
            }
        }

        // Analyze memory metrics
        if let Some(latest_memory) = self.get_latest_memory_metric(report) {
            if let Some(heap_bottleneck) = self.analyze_heap_bottleneck(&latest_memory) {
                bottlenecks.push(heap_bottleneck);
            }
        }

        // Analyze application-specific metrics
        bottlenecks.extend(self.analyze_application_bottlenecks(report)?);

        Ok(bottlenecks)
    }

    /// Analyze CPU bottleneck
    fn analyze_cpu_bottleneck(&self, system_metric: &SystemMetric) -> Option<PerformanceBottleneck> {
        let cpu_usage = system_metric.cpu_usage_percent;
        let severity = self.get_severity(cpu_usage, &self.config.cpu_thresholds);

        if severity == Severity::Low {
            return None;
        }

        let recommendations = match severity {
            Severity::Medium => vec![
                "考虑优化 CPU 密集型算法".to_string(),
                "检查是否有不必要的循环或递归".to_string(),
            ],
            Severity::High => vec![
                "立即优化 CPU 密集型操作".to_string(),
                "考虑使用多线程或异步处理".to_string(),
                "检查是否有死循环或无限递归".to_string(),
            ],
            Severity::Critical => vec![
                "紧急优化 CPU 使用".to_string(),
                "暂停非关键操作".to_string(),
                "考虑水平扩展".to_string(),
            ],
            _ => vec![],
        };

        Some(PerformanceBottleneck {
            bottleneck_type: BottleneckType::CPU,
            severity,
            description: format!("CPU 使用率过高: {:.1}%", cpu_usage),
            metric_name: "cpu_usage_percent".to_string(),
            current_value: cpu_usage,
            expected_value: self.config.cpu_thresholds.low,
            impact_percentage: ((cpu_usage - self.config.cpu_thresholds.low) / 
                              (100.0 - self.config.cpu_thresholds.low)) * 100.0,
            recommendations,
            detected_at: SystemTime::now(),
        })
    }

    /// Analyze memory bottleneck
    fn analyze_memory_bottleneck(&self, system_metric: &SystemMetric) -> Option<PerformanceBottleneck> {
        let memory_usage = system_metric.memory_usage_percent;
        let severity = self.get_severity(memory_usage, &self.config.memory_thresholds);

        if severity == Severity::Low {
            return None;
        }

        let recommendations = match severity {
            Severity::Medium => vec![
                "检查内存使用情况".to_string(),
                "清理不必要的缓存".to_string(),
            ],
            Severity::High => vec![
                "立即释放不必要的内存".to_string(),
                "检查内存泄漏".to_string(),
                "优化数据结构".to_string(),
            ],
            Severity::Critical => vec![
                "紧急释放内存".to_string(),
                "重启应用程序".to_string(),
                "增加系统内存".to_string(),
            ],
            _ => vec![],
        };

        Some(PerformanceBottleneck {
            bottleneck_type: BottleneckType::Memory,
            severity,
            description: format!("内存使用率过高: {:.1}%", memory_usage),
            metric_name: "memory_usage_percent".to_string(),
            current_value: memory_usage,
            expected_value: self.config.memory_thresholds.low,
            impact_percentage: ((memory_usage - self.config.memory_thresholds.low) / 
                              (100.0 - self.config.memory_thresholds.low)) * 100.0,
            recommendations,
            detected_at: SystemTime::now(),
        })
    }

    /// Analyze disk bottleneck
    fn analyze_disk_bottleneck(&self, system_metric: &SystemMetric) -> Option<PerformanceBottleneck> {
        let disk_usage = system_metric.disk_usage_percent;
        
        if disk_usage < 80.0 {
            return None;
        }

        let severity = if disk_usage >= 95.0 {
            Severity::Critical
        } else if disk_usage >= 90.0 {
            Severity::High
        } else {
            Severity::Medium
        };

        let recommendations = match severity {
            Severity::Medium => vec![
                "清理临时文件".to_string(),
                "检查日志文件大小".to_string(),
            ],
            Severity::High => vec![
                "立即清理磁盘空间".to_string(),
                "移动大文件到其他位置".to_string(),
                "压缩或删除旧文件".to_string(),
            ],
            Severity::Critical => vec![
                "紧急清理磁盘空间".to_string(),
                "停止非关键写操作".to_string(),
                "扩展磁盘容量".to_string(),
            ],
            _ => vec![],
        };

        Some(PerformanceBottleneck {
            bottleneck_type: BottleneckType::Disk,
            severity,
            description: format!("磁盘使用率过高: {:.1}%", disk_usage),
            metric_name: "disk_usage_percent".to_string(),
            current_value: disk_usage,
            expected_value: 70.0,
            impact_percentage: ((disk_usage - 70.0) / 30.0) * 100.0,
            recommendations,
            detected_at: SystemTime::now(),
        })
    }

    /// Analyze network bottleneck
    fn analyze_network_bottleneck(&self, _system_metric: &SystemMetric) -> Option<PerformanceBottleneck> {
        // Network bottleneck analysis would require more sophisticated metrics
        // This is a placeholder for future implementation
        None
    }

    /// Analyze heap memory bottleneck
    fn analyze_heap_bottleneck(&self, memory_metric: &MemoryMetric) -> Option<PerformanceBottleneck> {
        if memory_metric.heap_total_bytes == 0 {
            return None;
        }

        let heap_usage_percent = (memory_metric.heap_used_bytes as f64 / 
                                 memory_metric.heap_total_bytes as f64) * 100.0;

        if heap_usage_percent < 80.0 {
            return None;
        }

        let severity = if heap_usage_percent >= 95.0 {
            Severity::Critical
        } else if heap_usage_percent >= 90.0 {
            Severity::High
        } else {
            Severity::Medium
        };

        Some(PerformanceBottleneck {
            bottleneck_type: BottleneckType::Memory,
            severity,
            description: format!("堆内存使用率过高: {:.1}%", heap_usage_percent),
            metric_name: "heap_usage_percent".to_string(),
            current_value: heap_usage_percent,
            expected_value: 70.0,
            impact_percentage: ((heap_usage_percent - 70.0) / 30.0) * 100.0,
            recommendations: vec![
                "检查内存泄漏".to_string(),
                "优化对象生命周期管理".to_string(),
                "减少大对象分配".to_string(),
            ],
            detected_at: SystemTime::now(),
        })
    }

    /// Analyze application-specific bottlenecks
    fn analyze_application_bottlenecks(&self, _report: &PerformanceReport) -> Result<Vec<PerformanceBottleneck>> {
        // Application-specific analysis would be implemented here
        // This could include parse time analysis, graph update performance, etc.
        Ok(Vec::new())
    }

    /// Get severity level based on value and thresholds
    fn get_severity(&self, value: f64, thresholds: &ThresholdConfig) -> Severity {
        if value >= thresholds.critical {
            Severity::Critical
        } else if value >= thresholds.high {
            Severity::High
        } else if value >= thresholds.medium {
            Severity::Medium
        } else {
            Severity::Low
        }
    }

    /// Calculate overall performance score
    fn calculate_performance_score(&self, _report: &PerformanceReport, bottlenecks: &[PerformanceBottleneck]) -> f64 {
        if bottlenecks.is_empty() {
            return 100.0;
        }

        let total_impact: f64 = bottlenecks.iter()
            .map(|b| match b.severity {
                Severity::Critical => 40.0,
                Severity::High => 25.0,
                Severity::Medium => 15.0,
                Severity::Low => 5.0,
            })
            .sum();

        (100.0 - total_impact).max(0.0)
    }

    /// Analyze performance trends
    fn analyze_trends(&self) -> Result<PerformanceTrends> {
        if self.historical_data.len() < self.config.min_samples_for_trend {
            return Ok(PerformanceTrends {
                cpu_trend: TrendDirection::Stable,
                memory_trend: TrendDirection::Stable,
                response_time_trend: TrendDirection::Stable,
                error_rate_trend: TrendDirection::Stable,
            });
        }

        // Analyze CPU trend
        let cpu_values: Vec<f64> = self.historical_data.iter()
            .filter_map(|report| self.get_latest_system_metric(report))
            .map(|metric| metric.cpu_usage_percent)
            .collect();

        let cpu_trend = self.calculate_trend(&cpu_values);

        // Analyze memory trend
        let memory_values: Vec<f64> = self.historical_data.iter()
            .filter_map(|report| self.get_latest_system_metric(report))
            .map(|metric| metric.memory_usage_percent)
            .collect();

        let memory_trend = self.calculate_trend(&memory_values);

        Ok(PerformanceTrends {
            cpu_trend,
            memory_trend,
            response_time_trend: TrendDirection::Stable, // Placeholder
            error_rate_trend: TrendDirection::Stable,    // Placeholder
        })
    }

    /// Calculate trend direction from values
    fn calculate_trend(&self, values: &[f64]) -> TrendDirection {
        if values.len() < 3 {
            return TrendDirection::Stable;
        }

        let mid_point = values.len() / 2;
        let first_half: f64 = values[..mid_point].iter().sum::<f64>() / mid_point as f64;
        let second_half: f64 = values[mid_point..].iter().sum::<f64>() / (values.len() - mid_point) as f64;

        let change_percent = ((second_half - first_half) / first_half) * 100.0;

        if change_percent.abs() < 5.0 {
            TrendDirection::Stable
        } else if change_percent > 15.0 {
            TrendDirection::Degrading
        } else if change_percent < -15.0 {
            TrendDirection::Improving
        } else {
            TrendDirection::Volatile
        }
    }

    /// Generate optimization suggestions
    fn generate_optimization_suggestions(
        &self, 
        bottlenecks: &[PerformanceBottleneck], 
        trends: &PerformanceTrends
    ) -> Vec<OptimizationSuggestion> {
        let mut suggestions = Vec::new();

        // Generate suggestions based on bottlenecks
        for bottleneck in bottlenecks {
            match bottleneck.bottleneck_type {
                BottleneckType::CPU => {
                    suggestions.push(OptimizationSuggestion {
                        category: "CPU 优化".to_string(),
                        priority: bottleneck.severity.clone(),
                        description: "优化 CPU 密集型操作，考虑使用并行处理".to_string(),
                        expected_impact: "减少 20-40% CPU 使用率".to_string(),
                        difficulty: "中等".to_string(),
                        estimated_time: "1-2 周".to_string(),
                    });
                },
                BottleneckType::Memory => {
                    suggestions.push(OptimizationSuggestion {
                        category: "内存优化".to_string(),
                        priority: bottleneck.severity.clone(),
                        description: "优化内存使用，检查内存泄漏".to_string(),
                        expected_impact: "减少 15-30% 内存使用".to_string(),
                        difficulty: "中等".to_string(),
                        estimated_time: "1-3 周".to_string(),
                    });
                },
                _ => {}
            }
        }

        // Generate suggestions based on trends
        match trends.cpu_trend {
            TrendDirection::Degrading => {
                suggestions.push(OptimizationSuggestion {
                    category: "性能趋势".to_string(),
                    priority: Severity::Medium,
                    description: "CPU 使用率呈上升趋势，需要预防性优化".to_string(),
                    expected_impact: "防止未来性能问题".to_string(),
                    difficulty: "低".to_string(),
                    estimated_time: "1 周".to_string(),
                });
            },
            _ => {}
        }

        suggestions
    }

    /// Get latest system metric from report
    fn get_latest_system_metric(&self, _report: &PerformanceReport) -> Option<SystemMetric> {
        // This would extract the latest system metric from the report
        // Placeholder implementation
        None
    }

    /// Get latest memory metric from report
    fn get_latest_memory_metric(&self, _report: &PerformanceReport) -> Option<MemoryMetric> {
        // This would extract the latest memory metric from the report
        // Placeholder implementation
        None
    }
}

impl Default for BottleneckAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}
